OpenRaiser/Intern-Atlas huggingface dataset:
A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision
Venue: arXiv cs.CV 2017
Year: 2017

Being inspired by child's learning experience - taught first and followed by observation and questioning, we investigate a critically supervised learning methodology for object detection in this work. Specifically, we propose a taught-observe-ask (TOA) method that consists of several novel components such as negative object proposal, critical example mining, and machine-guided question-answer (QA) labeling. To consider labeling time and performance jointly, new evaluation methods are developed to compare the performance of the TOA method, with the fully and weakly supervised learning methods. Extensive experiments are conducted on the PASCAL VOC and the Caltech benchmark datasets. The TOA method provides significantly improved performance of weakly supervision yet demands only about 3-6% of labeling time of full supervision. The effectiveness of each novel component is also analyzed.
An Empirical Study on Leanness and Flexibility in Distributed Software Development
Venue: arXiv cs.SE 2017
Year: 2017

Nowadays, many individuals and teams involved on projects are already using agile development techniques as part of their daily work. However, we have much less experience in how to scale and manage agile practices in distributed software development. Distributed and global development- that requiring attention to many technical, organizational, and cultural issues as the teams interact to cooperatively delivery the solution. Alongside, very large team sizes, teams of teams, and more complex management structures forcing additional attention to coordination and management. At this level, there is an increasing need to standardize best practices to avoid reinvention and miscommunication across artifacts and processes. Complexity issues in enterprise software delivery can have significant impact on the adoption of agile approaches. As a consequence, agile strategies will typically need to be evaluated, tailored, and perhaps combined with traditional approaches to suit the particular context. The characteristics of software products and software development processes open up new possibilities that are different from those offered in other domains to achieve leanness and flexibility. Whilst Lean principles are universal, a further understanding of the techniques required to apply such principles from a software development angle. Thus, the aim of this research is to identify, how leanness facilitate flexibility in distributed software development to speed-up development process.
Fuzzy clustering using linguistic-valued exponent
Venue: arXiv cs.LG 2017
Year: 2017

The purpose of this paper is to study the algorithm FCM and some of its famous innovations, analyse and discover the method of applying hedge algebra theory that uses algebra to represent linguistic-valued variables, to FCM. Then, this paper will propose a new FCM-based algorithm which uses hedge algebra to model FCM's exponent parameter. Finally, the design, analysis and implementation of the new algorithm as well some experimental results will be presented to prove our algorithm's capacity of solving clustering problems in practice.
The Bane of Low-Dimensionality Clustering
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we give a conditional lower bound of $n^{Ω(k)}$ on running time for the classic k-median and k-means clustering objectives (where n is the size of the input), even in low-dimensional Euclidean space of dimension four, assuming the Exponential Time Hypothesis (ETH). We also consider k-median (and k-means) with penalties where each point need not be assigned to a center, in which case it must pay a penalty, and extend our lower bound to at least three-dimensional Euclidean space. This stands in stark contrast to many other geometric problems such as the traveling salesman problem, or computing an independent set of unit spheres. While these problems benefit from the so-called (limited) blessing of dimensionality, as they can be solved in time $n^{O(k^{1-1/d})}$ or $2^{n^{1-1/d}}$ in d dimensions, our work shows that widely-used clustering objectives have a lower bound of $n^{Ω(k)}$, even in dimension four. We complete the picture by considering the two-dimensional case: we show that there is no algorithm that solves the penalized version in time less than $n^{o(\sqrt{k})}$, and provide a matching upper bound of $n^{O(\sqrt{k})}$. The main tool we use to establish these lower bounds is the placement of points on the moment curve, which takes its inspiration from constructions of point sets yielding Delaunay complexes of high complexity.
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning
Venue: arXiv cs.LG 2017
Year: 2017

Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router and a set of one or more function blocks. A function block may be any neural network - for example a fully-connected or a convolutional layer. Given an input the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively, terminating when a fixed recursion depth is reached. In this way the routing network dynamically composes different function blocks for each input. We employ a collaborative multi-agent reinforcement learning (MARL) approach to jointly train the router and function blocks. We evaluate our model against cross-stitch networks and shared-layer baselines on multi-task settings of the MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a significant improvement in accuracy, with sharper convergence. In addition, routing networks have nearly constant per-task training cost while cross-stitch networks scale linearly with the number of tasks. On CIFAR-100 (20 tasks) we obtain cross-stitch performance levels with an 85% reduction in training time.
Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities
Venue: arXiv cs.DB 2017
Year: 2017

Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary points in time. We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques. The current modus operandi for filtering activities from event logs is to simply filter out infrequent activities. We show that frequency-based filtering of activities does not solve the problems that are caused by chaotic activities. Moreover, we propose a novel technique to filter out chaotic activities from event logs. We evaluate this technique on a collection of seventeen real-life event logs that originate from both the business process management domain and the smart home environment domain. As demonstrated, the developed activity filtering methods enable the discovery of process models that are more behaviorally specific compared to process models that are discovered using standard frequency-based filtering.
BoostClean: Automated Error Detection and Repair for Machine Learning
Venue: arXiv cs.DB 2017
Year: 2017

Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sources, each susceptible to different types of inconsistencies, and new data streams during prediction time, the model may encounter previously unseen inconsistencies. An important class of such inconsistencies is domain value violations that occur when an attribute value is outside of an allowed domain. We explore automatically detecting and repairing such violations by leveraging the often available clean test labels to determine whether a given detection and repair combination will improve model accuracy. We present BoostClean which automatically selects an ensemble of error detection and repair combinations using statistical boosting. BoostClean selects this ensemble from an extensible library that is pre-populated general detection functions, including a novel detector based on the Word2Vec deep learning model, which detects errors across a diverse set of domains. Our evaluation on a collection of 12 datasets from Kaggle, the UCI repository, real-world data analyses, and production datasets that show that Boost- Clean can increase absolute prediction accuracy by up to 9% over the best non-ensembled alternatives. Our optimizations including parallelism, materialization, and indexing techniques show a 22.2x end-to-end speedup on a 16-core machine.
Phototactic Supersmarticles
Venue: arXiv cs.RO 2017
Year: 2017

Smarticles, or smart active particles, are small robots equipped with only basic movement and sensing abilities that are incapable of rotating or displacing individually. We study the ensemble behavior of smarticles, i.e., the behavior a collective of these very simple computational elements can achieve, and how such behavior can be implemented using minimal programming. We show that an ensemble of smarticles constrained to remain close to one another (which we call a supersmarticle), achieves directed locomotion toward or away from a light source, a phenomenon known as phototaxing. We present experimental and theoretical models of phototactic supersmarticles that collectively move with a directed displacement in response to light. The motion of the supersmarticle is approximately Brownian, and is a result of chaotic interactions among smarticles. The system can be directed by introducing asymmetries among the individual smarticle's behavior, in our case by varying activity levels in response to light, resulting in supersmarticle biased motion.
A Study of Optimizing Heterogeneous Resources for Open IoT
Venue: arXiv cs.DC 2017
Year: 2017

Recently, IoT technologies have been progressed, and many sensors and actuators are connected to networks. Previously, IoT services were developed by vertical integration style. But now Open IoT concept has attracted attentions which achieves various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. We also implemented elemental technologies of Tacit Computing. In this paper, we propose three layers optimizations to reduce operation cost and improve performance of Tacit computing service, in order to make as a continuous service of discovered devices by Tacit Computing. In optimization process, appropriate function allocations are calculated for device, network and cloud layer before full-scale operation.
Decentralised firewall for malware detection
Venue: arXiv cs.CR 2017
Year: 2017

This paper describes the design and development of a decentralized firewall system powered by a novel malware detection engine. The firewall is built using blockchain technology. The detection engine aims to classify Portable Executable (PE) files as malicious or benign. File classification is carried out using a deep belief neural network (DBN) as the detection engine. Our approach is to model the files as grayscale images and use the DBN to classify those images into the aforementioned two classes. An extensive data set of 10,000 files is used to train the DBN. Validation is carried out using 4,000 files previously unexposed to the network. The final result of whether to allow or block a file is obtained by arriving at a proof of work based consensus in the blockchain network.
Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples
Venue: arXiv cs.AI 2017
Year: 2017

In robotics, it is essential to be able to plan efficiently in high-dimensional continuous state-action spaces for long horizons. For such complex planning problems, unguided uniform sampling of actions until a path to a goal is found is hopelessly inefficient, and gradient-based approaches often fall short when the optimization manifold of a given problem is not smooth. In this paper we present an approach that guides the search of a state-space planner, such as A*, by learning an action-sampling distribution that can generalize across different instances of a planning problem. The motivation is that, unlike typical learning approaches for planning for continuous action space that estimate a policy, an estimated action sampler is more robust to error since it has a planner to fall back on. We use a Generative Adversarial Network (GAN), and address an important issue: search experience consists of a relatively large number of actions that are not on a solution path and a relatively small number of actions that actually are on a solution path. We introduce a new technique, based on an importance-ratio estimation method, for using samples from a non-target distribution to make GAN learning more data-efficient. We provide theoretical guarantees and empirical evaluation in three challenging continuous robot planning problems to illustrate the effectiveness of our algorithm.
The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning
Venue: arXiv cs.AI 2017
Year: 2017

Machine learning is usually defined in behaviourist terms, where external validation is the primary mechanism of learning. In this paper, I argue for a more holistic interpretation in which finding more probable, efficient and abstract representations is as central to learning as performance. In other words, machine learning should be extended with strategies to reason over its own learning process, leading to so-called meta-cognitive machine learning. As such, the de facto definition of machine learning should be reformulated in these intrinsically multi-objective terms, taking into account not only the task performance but also internal learning objectives. To this end, we suggest a "model entropy function" to be defined that quantifies the efficiency of the internal learning processes. It is conjured that the minimization of this model entropy leads to concept formation. Besides philosophical aspects, some initial illustrations are included to support the claims.
Semantic Web Today: From Oil Rigs to Panama Papers
Venue: arXiv cs.AI 2017
Year: 2017

The next leap on the internet has already started as Semantic Web. At its core, Semantic Web transforms the document oriented web to a data oriented web enriched with semantics embedded as metadata. This change in perspective towards the web offers numerous benefits for vast amount of data intensive industries that are bound to the web and its related applications. The industries are diverse as they range from Oil & Gas exploration to the investigative journalism, and everything in between. This paper discusses eight different industries which currently reap the benefits of Semantic Web. The paper also offers a future outlook into Semantic Web applications and discusses the areas in which Semantic Web would play a key role in the future.
On the Outage Probability Conjecture for MIMO Channels
Venue: arXiv cs.IT 2017
Year: 2017

Multiple-Input-Multiple-Output (MIMO) communication systems have seen wide application due to its performance benefits such as multiplexing gain. For MIMO systems with non-ergodic Gaussian channel, a conjecture regarding its outage probability has been proposed by Telatar in [1]. This conjecture has been proved for the special single-output case, and is in general assumed to be true. In this work, we analyze the special Two-Input-Multiple-Output (TIMO) case theoretically, and provide a counter-example to the conjecture. The counter-example is verified both theoretically and by numerical experiments. We also present a theoretical analysis for general MIMO case, including a method for calculation. This result rejects the decades-long conjecture and provides interesting insight into the symmetry of MIMO systems.
Secure Transmission in Linear Multihop Relaying Networks
Venue: arXiv cs.IT 2017
Year: 2017

This paper studies the design and secrecy performance of linear multihop networks, in the presence of randomly distributed eavesdroppers in a large-scale two-dimensional space. Depending on whether there is feedback from the receiver to the transmitter, we study two transmission schemes: on-off transmission (OFT) and non-on-off transmission (NOFT). In the OFT scheme, transmission is suspended if the instantaneous received signal-to-noise ratio (SNR) falls below a given threshold, whereas there is no suspension of transmission in the NOFT scheme. We investigate the optimal design of the linear multiple network in terms of the optimal rate parameters of the wiretap code as well as the optimal number of hops. These design parameters are highly interrelated since more hops reduces the distance of per-hop communication which completely changes the optimal design of the wiretap coding rates. Despite the analytical difficulty, we are able to characterize the optimal designs and the resulting secure transmission throughput in mathematically tractable forms in the high SNR regime. Our numerical results demonstrate that our analytical results obtained in the high SNR regime are accurate at practical SNR values. Hence, these results provide useful guidelines for designing linear multihop networks with targeted physical layer security performance.
The TensorFlow Partitioning and Scheduling Problem: It's the Critical Path!
Venue: arXiv cs.DC 2017
Year: 2017

State-of-the-art data flow systems such as TensorFlow impose iterative calculations on large graphs that need to be partitioned on heterogeneous devices such as CPUs, GPUs, and TPUs. However, partitioning can not be viewed in isolation. Each device has to select the next graph vertex to be executed, i.e., perform local scheduling decisions. Both problems, partitioning and scheduling, are NP-complete by themselves but have to be solved in combination in order to minimize overall execution time of an iteration. In this paper, we propose several heuristic strategies to solve the partitioning and scheduling problem in TensorFlow. We simulate the performance of the proposed strategies in heterogeneous environments with communication-intensive workloads that are common to TensorFlow. Our findings indicate that the best partitioning and scheduling heuristics are those that focus on minimizing the execution time of the critical path in the graph. Those strategies provide a speed-up of up to 4 times in comparison to strategies that are agnostic to the critical path, such as hash-based partitioning and FIFO scheduling.
Almost Polynomial Hardness of Node-Disjoint Paths in Grids
Venue: arXiv cs.DS 2017
Year: 2017

In the classical Node-Disjoint Paths (NDP) problem, we are given an $n$-vertex graph $G=(V,E)$, and a collection $M=\{(s_1,t_1),\ldots,(s_k,t_k)\}$ of pairs of its vertices, called source-destination, or demand pairs. The goal is to route as many of the demand pairs as possible, where to route a pair we need to select a path connecting it, so that all selected paths are disjoint in their vertices. The best current algorithm for NDP achieves an $O(\sqrt{n})$-approximation, while, until recently, the best negative result was a factor $Ω(\log^{1/2-ε}n)$-hardness of approximation, for any constant $ε$, unless $NP \subseteq ZPTIME(n^{poly \log n})$. In a recent work, the authors have shown an improved $2^{Ω(\sqrt{\log n})}$-hardness of approximation for NDP, unless $NP\subseteq DTIME(n^{O(\log n)})$, even if the underlying graph is a subgraph of a grid graph, and all source vertices lie on the boundary of the grid. Unfortunately, this result does not extend to grid graphs. The approximability of the NDP problem on grid graphs has remained a tantalizing open question, with the best current upper bound of $\tilde{O}(n^{1/4})$, and the best current lower bound of APX-hardness. In this paper we come close to resolving the approximability of NDP in general, and NDP in grids in particular. Our main result is that NDP is $2^{Ω(\log^{1-ε} n)}$-hard to approximate for any constant $ε$, assuming that $NP\not\subseteq RTIME(n^{poly\log n})$, and that it is $n^{Ω(1/(\log \log n)^2)}$-hard to approximate, assuming that for some constant $δ>0$, $NP \not \subseteq RTIME(2^{n^δ})$. These results hold even for grid graphs and wall graphs, and extend to the closely related Edge-Disjoint Paths problem, even in wall graphs.
Full-Duplex Cloud Radio Access Network: Stochastic Design and Analysis
Venue: arXiv cs.IT 2017
Year: 2017

Full-duplex (FD) has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in cellular networks, is however largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process (PPP)-based abstraction model of multi-antenna radio units (RUs) and user equipments (UEs). We consider different disjoint and user-centric approaches towards the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo (MC) simulations. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient-capacity fronthaul links and advanced interference cancellation capabilities.
On Structural Parameterizations of the Edge Disjoint Paths Problem
Venue: arXiv cs.DS 2017
Year: 2017

In this paper we revisit the classical Edge Disjoint Paths (EDP) problem, where one is given an undirected graph G and a set of terminal pairs P and asks whether G contains a set of pairwise edge-disjoint paths connecting every terminal pair in P. Our focus lies on structural parameterizations for the problem that allow for efficient (polynomial-time or fpt) algorithms. As our first result, we answer an open question stated in Fleszar, Mnich, and Spoerhase (2016), by showing that the problem can be solved in polynomial time if the input graph has a feedback vertex set of size one. We also show that EDP parameterized by the treewidth and the maximum degree of the input graph is fixed-parameter tractable. Having developed two novel algorithms for EDP using structural restrictions on the input graph, we then turn our attention towards the augmented graph, i.e., the graph obtained from the input graph after adding one edge between every terminal pair. In constrast to the input graph, where EDP is known to remain NP-hard even for treewidth two, a result by Zhou et al. (2000) shows that EDP can be solved in non-uniform polynomial time if the augmented graph has constant treewidth; we note that the possible improvement of this result to an fpt-algorithm has remained open since then. We show that this is highly unlikely by establishing the W[1]-hardness of the problem parameterized by the treewidth (and even feedback vertex set) of the augmented graph. Finally, we develop an fpt-algorithm for EDP by exploiting a novel structural parameter of the augmented graph.